System and method for adaptive interference cancelling

ABSTRACT

An adaptive system and method for reducing interference in a signal received from an array of sensors. Adaptive filters are used to generate cancelling signals that closely approximate the interference present in the received signal. The adaptive filter weights are converted into the frequency domain where the frequency representation values in a selected frequency range are truncated to avoid signal leakage involving narrow band signals. Deodorizing filters are used to produce the cancelling signals having a flat frequency spectrum. Normalized power difference is used limit the operation of the adaptive filters to the case where there is some directional interference to be eliminated.

This application is a continuation of Ser. No. 08/672,889 filed Jun. 27,1996, now U.S. Pat. No. 5,825,898, issued Oct. 20, 1998.

BACKGROUND OF THE INVENTION

The present invention relates generally to signal processing, and morespecifically to an adaptive signal processing system and method forreducing interference in a received signal.

There are many instances where it is desirable to have a sensor capableof receiving an information signal from a particular signal source wherethe environment includes sources of interference signals at locationsdifferent from that of the signal source. One such instance is the useof microphones to record a particular party's speech in a room wherethere are other parties speaking simultaneously, causing interference inthe received signals.

If one knows the exact characteristics of the interference, one can usea fixed-weight filter to suppress it. But it is often difficult topredict the exact characteristics of the interference because they mayvary according to changes in the interference sources, the backgroundnoise, acoustic environment, orientation of the sensor with respect tothe signal source, the transmission paths from the signal source to thesensor, and many other factors. Therefore, in order to suppress suchinterference, an adaptive system that can change its own parameters inresponse to a changing environment is needed.

An adaptive filter is an adaptive system that can change its ownfiltering characteristics in order to produce a desired response.Typically, the filter weights defining the. characteristics of anadaptive filter are continuously updated so that the difference betweena signal representing a desired response and an output signal of theadaptive filter is minimized.

The use of adaptive filters for reducing interference in a receivedsignal has been known in the art as adaptive noise cancelling. It isbased on the idea of cancelling a noise component of a received signalfrom the direction of a signal source by sampling the noiseindependently of the source signal and modifying the sampled noise toapproximate the noise component in the received signal using an adaptivefilter. For a seminal article on adaptive noise cancelling, see B.Widrow et al., Adaptive Noise Cancelling: Principles and Applications,Proc. IEEE 63:1692-1716, 1975.

A basic configuration for adaptive noise cancelling has a primary inputreceived by a microphone directed to a desired signal source and areference input received independently by another microphone directed toa noise source. The primary input contains both a source signalcomponent originating from the signal source and a noise componentoriginating from the noise source. The noise component is different fromthe reference input representing the noise source itself because thenoise signal must travel from the noise source to the signal source inorder to be included as the noise component.

The noise component, however, is likely to have some correlation withthe reference input because both of them originate from the same noisesource. Thus, a filter can be used to filter the reference input togenerate a cancelling signal approximating the noise component. Theadaptive filter does this dynamically by generating an output signalwhich is the difference between the primary input and the cancellingsignal, and by adjusting its filter weights to minimize the mean-squarevalue of the output signal. When the filter weights settle, the outputsignal effectively replicates the source signal substantially free ofthe noise component because the cancelling signal closely tracks thenoise component.

Adaptive noise cancelling can be combined with beamforming, a knowntechnique of using an array of sensors to improve reception of signalscoming from a specific direction. A beamformer is a spatial filter thatgenerates a single channel from multiple channels received throughmultiple sensors by filtering the individual multiple channels andcombining them in such a way as to extract signals coming from aspecific direction. Thus, a beamformer can change the direction ofreceiving sensitivity without physically moving the array of sensors.For details on beamforming, see B. D. Van Veen and K. M. Buckley,Beamforming: Versatile Approach to Spatial Filtering, IEEE ASSP Mag.5(2), 4-24.

Since the beamformer can effectively be pointed in many directionswithout physically moving its sensors, the beamformer can be combinedwith adaptive noise cancelling to form an adaptive beamformer that cansuppress specific directional interference rather than generalbackground noise. The beamformer can provide the primary input byspatially filtering input signals from an array of sensors so that itsoutput represents a signal received in the direction of a signal source.Similarly, the beamformer can provide the reference input by spatiallyfiltering the sensor signals so that the output represents a signalreceived in the direction of interference sources. For a seminal articleon adaptive beamformers, see L. J. Griffiths & C. W. Jim, An AlternativeApproach to Linearly Constrained Adaptive Beamforming, IEEE Trans. Ant.Prop. AP-30:27-34, 1982.

One problem with a conventional adaptive beamformer is that its outputcharacteristics change depending on input frequencies and sensordirections with respect to interference sources. This is due to thesensitivity of a beamformer to different input frequencies and sensordirections. A uniform output behavior of a system over all inputfrequencies of interest and over all sensor directions is clearlydesirable in a directional microphone system where faithful reproductionof a sound signal is required regardless of where the microphones arelocated.

Another problem with adaptive beamforming is “signal leakage”. Adaptivenoise cancelling is based on an assumption that the reference inputrepresenting noise sources is uncorrelated with the source signalcomponent in the primary input, meaning that the reference input shouldnot contain the source signal. But this “signal free” reference inputassumption is violated in any real environment. Any mismatch in themicrophones (amplitude or phase) or their related analog front end, anyreverberation caused by the surroundings or a mechanical structure, andeven any mechanical coupling in the physical microphone structure willlikely cause “signal leakage” from the signal source into the referenceinput. If there is any correlation between the reference input and thesource signal component in the primary input, the adaptation process bythe adaptive filter causes cancellation of the source signal component,resulting in distortion and degradation in performance.

It is also important to confine the adaptation process to the case wherethere is at least some directional interference to be eliminated. Sincenondirectional noise, such as wind noise or vibration noise induced bythe mechanical structure of the system, is typically uncorrelated withthe noise component of the received signal, the adaptive filter cannotgenerate a cancelling signal approximating the noise component.

Prior art suggests inhibiting the adaptation process of an adaptivefilter when the signal-to-noise ratio (SNR) is high based on theobservation that a strong source signal tends to leak into the referenceinput. For example, U.S. Pat. No. 4,956,867 describes the use ofcross-correlation between two sensors to inhibit the adaptation processwhen the SNR is high.

But the prior art approach fails to consider the effect of directionalinterference because the SNR-based approach considers onlynondirectional noise. Since nondirectional. noise is not correlated tothe noise component of the received signal, the adaptation processsearches in vain for new filter weights, which often results incancelling the source signal component of the received signal.

The prior art approach also fails to consider signal leakage when thesource signal is of a narrow bandwidth. In a directional microphoneapplication, the source signal often contains a narrow band signal, suchas speech signal, with its power spectral density concentrated in anarrow frequency range. When signal leakage occurs due to a strongnarrow band signal, the prior art approach may not inhibit theadaptation process because the overall signal strength of such narrowband signal may not high enough. The source signal component of thereceived signal is cancelled as a result, and if the source signal is aspeech signal, degradation in speech intelligibility occurs.

Therefore, there exists a need for an adaptive system that can suppressdirectional interference in a received signal with a uniform frequencybehavior over a wide angular distribution of interference sources.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to suppressinterference in a received signal using an adaptive filter forprocessing inputs from an array of sensors.

Another object of the invention is to limit the adaptation process ofsuch adaptive filter to the case where there is at least somedirectional interference to be eliminated.

A further object of the invention is to control the adaptation processto prevent signal leakage for narrow band signals.

Another object is to produce an output with a uniform frequency behaviorin all directions from the sensor array.

These and other objects are achieved in accordance with the presentinvention, which uses a system for processing digital data representingsignals received from an array of sensors. The system includes a mainchannel matrix unit for generating a main channel representing signalsreceived in the direction of a signal source where the main channel hasa source signal component and an interference signal component. Thesystem includes a reference channel matrix unit for generating at leastone reference channel where each reference channel represents signalsreceived in directions other than that of the signal source. The systemuses adaptive filters for generating cancelling signals approximatingthe interference signal component of the main channel and a differenceunit for generating a digital output signal by subtracting thecancelling signals from the main channel. Each adaptive filter hasweight updating means for finding new filter weights based on the outputsignal. The system includes weight constraining means for truncating thenew filter weight values to predetermined threshold values when each ofthe new filter weight value exceeds the corresponding threshold value.

The system may further include at least one decolorizing filter forgenerating a flat-frequency reference channel. The system may furtherinclude inhibiting means for estimating the power of the main channeland the power of the reference channels and for generating an inhibitsignal to the weight updating means based on normalized power differencebetween the main channel and the reference channels.

The system produces an output substantially free of directionalinterference with a uniform frequency behavior in all directions fromthe system.

The objects are also achieved in accordance with the present inventionusing a method, which can readily be implemented in a programcontrolling a commercially available DSP processor.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will bemore readily apparent from the following detailed description of theinvention in which:

FIG. 1 is a block diagram of an overall system;

FIG. 2 is a block diagram of a sampling unit;

FIG. 3 is a block diagram of an alternative embodiment of a samplingunit;

FIG. 4 is a schematic depiction of tapped delay lines used in a mainchannel matrix and a reference matrix unit;

FIG. 5 is a schematic depiction of a main channel matrix unit;

FIG. 6 is a schematic depiction of a reference channel matrix unit;

FIG. 7 is a schematic depiction of a decolorizing

FIG. 8 is a schematic depiction of an inhibiting unit based ondirectional interference;

FIG. 9 is a schematic depiction of a frequency-selective constraintadaptive filter;

FIG. 10 is a block diagram of a frequency-selective weight-constraintunit;

FIG. 11 is a flow chart depicting the operation of a program that can beus ed to implement the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of a system in accordance with a preferredembodiment of the present invention. The system illustrated has a sensorarray 1, a sampling unit 2, a main channel matrix unit 3, a referencechannel matrix unit 4, a set of decolorizing filters 5, a set offrequency-selective constrained adaptive filters 6, a delay 7, adifference unit 8, an inhibiting unit 9, and an output D/A unit 10.

Sensor array 1, having individual sensors 1 a-1 d, receives signals froma signal source on-axis from the system and from interference sourceslocated off-axis from the system. The sensor array is connected tosampling unit 2 for sampling the received signals, having individualsampling elements, 2 a-2 d, where each element is connected to thecorresponding individual sensor to produce digital signals 11.

The outputs of sampling unit 2 are connected to main channel matrix unit3 producing a main channel 12 representing signals received in thedirection of a source. The main channel contains both a source signalcomponent and an interference signal component.

The outputs of sampling unit 2 are also connected reference channelmatrix unit 4, which generates reference channels 13 representingsignals received from directions other that of the signal source. Thus,the reference channels represent interference signals.

The reference channels are filtered through decolorizing filters 5,which generate flat-frequency reference channels 14 having a frequencyspectrum whose magnitude is substantially flat over a frequency range ofinterest. Flat-frequency reference channels 14 are fed into the set offrequency-selective constraint adaptive filters 6, which generatecancelling signals 15.

In the mean time, main channel 12 is delayed through delay 7 so that itis synchronized with cancelling signals 15. Difference unit 8 thensubtracts cancelling signals 15 from the delayed main channel togenerate an digital output signal 16, which is converted by D/A unit 10into analog form. Digital output signal 15 is fed back to the adaptivefilters to update the filter weights of the adaptive filters.

Flat-frequency reference channels 14 are fed to inhibiting unit 9, whichestimates the power of each flat-frequency reference channel as well asthe power of the main channel and generates an inhibit signal 19 toprevent signal leakage.

FIG. 2 depicts a preferred embodiment of the sampling unit. A sensorarray 21, having sensor elements 21 a-21 d, is connected to an analogfront end 22, having amplifier elements 22 a-22 d, where each amplifierelement is connected to the output of the corresponding sensor element.In a directional microphone application, each sensor can be either adirectional or omnidirectional microphone. The analog front endamplifies the received analog sensor signals to match the inputrequirement of the sampling elements. The outputs from the analog frontends are connected to a set of delta-sigma A/D converters, 23, whereeach converter samples and digitizes the amplified analog signals. Thedelta-sigma sampling is a well-known A/D technique using bothoversampling and digital filtering. For details on delta-sigma A/Dsampling, see Crystal Semiconductor Corporation, Application Note:Delta-Sigma Techniques, 1989.

FIG. 3 shows an alternative embodiment of the sampling unit. A sensorarray 31, having sensor elements 31 a-32 d, is connected to an amplifier32, having amplifier elements 32 a-32 d, where each amplifier elementamplifies the received signals from the corresponding sensor element.The outputs of the amplifier are connected to a sample & hold (S/H) unit33 having sample & hold elements 33 a-33 d, where each S/H elementsamples the amplified analog signal from the corresponding amplifierelement to produce a discrete signal. The outputs from the S/H unit aremultiplexed into a single signal through a multiplexor 34. The output ofthe multiplexor is connected to a conventional A/D converter 35 toproduce a digital signal.

FIG. 4 is a schematic depiction of tapped delay lines used in the mainchannel matrix unit and the reference channel matrix in accordance witha preferred embodiment of the present invention. The tapped delay lineused here is defined as a nonrecursive digital filter, also known in theart as a transversal filter, a finite impulse response filter or an FIRfilter. The illustrated embodiment has 4 tapped delay lines, 40 a-40 d.Each tapped delay line includes delay elements 41, multipliers 42 andadders 43. Digital signals, 44 a-44 d, are fed into the set of tappeddelay lines 40 a-40 d. Delayed signals through delay elements 41 aremultiplied by filter coefficients, F_(ij), 45 and added to produceoutputs, 46 a-46 d.

The n-th sample of an output from the i-th tapped delay line, Y_(i)(n),can then be expressed as:

Y_(i)(n)=Σ^(k) _(j−0)F_(i,j)X_(i)(n-j),

where k is the length of the filter, and X_(i)(n) is the n-th sample ofan input to the i-th tapped delay line.

FIG. 5 depicts the main channel matrix unit for generating a mainchannel in accordance with a preferred embodiment of the presentinvention. The unit has tapped delay lines, 50 a-50 d, as an inputsection taking inputs 51 a-51 d from the sampling unit. Its outputsection includes multipliers, 52 a-52 d, where each multiplier isconnected to the corresponding tapped delay line and an adder 53, whichsums all output signals from the multipliers. The unit generates a mainchannel 54, as a weighted sum of outputs from all multipliers. Thefilter weights 55 a-55 d can be any combination of fractions as long astheir sum is 1. For example, if 4 microphones are used, the embodimentmay use the filter weights of ¼ in order to take into account of thecontribution of each microphone.

The unit acts as a beamformer, a spatial filter which filters a signalcoming in all directions to produce a signal coming in a specificdirection without physically moving the sensor array. The coefficientsof the tapped delay lines and the filter weights are set in such a waythat the received signals are spatially filtered to maximize thesensitivity toward the signal source.

Since some interference signals find their way to reach the signalsource due to many factors such as the reverberation of a room, mainchannel 54 representing the received signal in the direction of thesignal source contains not only a source signal component, but also aninterference signal component.

FIG. 6 depicts the reference channel matrix unit for generatingreference matrix channels in accordance with a preferred embodiment ofthe present invention. It has tapped delay lines, 60 a-60 d, as an inputsection taking inputs 61 a-61 d from the sampling unit. The same tappeddelay lines as that of FIG. 4 may be used, in which case the tappeddelay lines may be shared by the main and reference channel matrixunits.

Its output section includes multipliers, 62 a-62 d, 63 a-63 d, 64 a-64 dand adders 65 a-65 c, where each multiplier is connected to thecorresponding tapped delay line and adder. The unit acts as a beamformerwhich generates the reference channels 66 a-66 c representing signalsarriving off-axis from the signal source by obtaining the weighteddifferences of certain combinations of outputs from the tapped delaylines. The filter weight combinations can be any numbers as long astheir sum of filter weights for combining a given reference channel is0. For example, the illustrated embodiment may use a filter weightcombination, (W11, W12, W13, W14)=(0.25, 0.25, 0.25, −0.75), in order tocombine signals 61 a-61 d to produce reference channel 66 a.

The net effect is placing a null (low sensitivity) in the receiving gainof the beamformer toward the signal source. As a result, the referencechannels represent interference signals in directions other than that ofthe signal source. In other words, the unit “steers” the input digitaldata to obtain interference signals without physically moving the sensorarray.

FIG. 7 is a schematic depiction of the decolorizing filter in accordancewith a preferred embodiment of the present invention. It is a tappeddelay line including delay elements 71, multipliers 72 and adders 73. Areference channel 74 is fed into the tapped delay line. Delayed signalsare multiplied by filter coefficients, F_(i), 75 and added to produce anoutput 76. The filter coefficients are set in such a way that the filteramplifies the low-magnitude frequency components of an input signal toobtain an output signal having a substantially flat frequency spectrum.

As mentioned before in the background section, the output of aconventional adaptive beamformer suffers a non-uniform frequencybehavior. This is because the reference channels do not have a flatfrequency spectrum. The receiving sensitivity of a beamformer toward aparticular angular direction is often described in terms of a gaincurve. As mentioned before, the reference channel is obtained by placinga null in the gain curve (making the sensor array insensitive) in thedirection of the signal source. The resulting gain curve has a lowergain for lower frequency signals than higher frequency signals. Sincethe reference channel is modified to generate a cancelling signal, anon-flat frequency spectrum of the reference channel is translated to anon-uniform frequency behavior in the system output.

The decolorizing filter is a fixed-coefficient filter which flattens thefrequency spectrum of the reference channel (thus “decolorizing” thereference channel) by boosting the low frequency portion of thereference channel. By adding the decolorizing filters to all outputs ofthe reference channel matrix unit, a substantially flat frequencyresponse in all directions is obtained.

The decolorizing filter in the illustrated embodiment uses a tappeddelay line filter which is the same as a finite impulse response (FIR)filter, but other kinds of filters such as an infinite impulse response(IIR) filter can also be used for the decolorizing filter in analternative embodiment.

FIG. 8 depicts schematically the inhibiting unit in accordance with apreferred embodiment of the present invention. It includes powerestimation units 81, 82 which estimate the power of a main channel 83and each reference channel 84, respectively. A sample power estimationunit 85 calculates the power of each sample. A multiplier 86 multipliesthe power of each sample by a fraction, α, which is the reciprocal ofthe number of samples for a given averaging period to obtain an averagesample power 87. An adder 88 adds the average sample power to the outputof another multiplier 89 which multiplies a previously calculated mainchannel power average 90 by (1−α). A new main channel power average isobtained by (new sample power)×α+(old power average)×(1−α). For example,if a 100− sample average is used, α=0.01. The updated power average willbe (new sample power)×0.01+(old power average)×0.99. In this way, theupdated power average will be available at each sampling instant ratherthan after an averaging period. Although the illustrated embodimentshows an on-the-fly estimation method of the power average, other kindsof power estimation methods can also be used in an alternativeembodiment.

A multiplier 91 multiplies the main channel power 89 with a threshold 92to obtain a normalized main channel power average 93. An adder 94subtracts reference channel power averages 95 from the normalized mainchannel power average 93 to produce a difference 96. If the differenceis positive, a comparator 97 generates an inhibit signal 98. The inhibitsignal is provided to the adaptive filters to stop the adaptationprocess to prevent signal leakage.

Although the illustrated embodiment normalizes the main channel poweraverage, an alternative embodiment may normalize the reference channelpower average instead of the main channel power average. For example, ifthe threshold 92 in the illustrated embodiment is 0.25, the same effectcan be obtained in the alternative embodiment by normalizing eachreference channel power average by multiplying it by 4.

This inhibition approach is different from the prior art SNR-basedinhibition approach mentioned in the background section in that itdetects the presence of significant directional interference which theprior art approach does not consider. As a result, thedirectional-interference-based inhibition approach stops the adaptationprocess when there is no significant directional interference to beeliminated, whereas the prior art approach does not.

For example, where there is a weak source signal (e.g. during speechintermission) and there is almost no directional interference exceptsome uncorrelated noise (such as noise due to wind or mechanicalvibrations on the sensor structure), the SNR-based approach would allowthe adaptive filter to continue adapting due to the small SNR. Thecontinued adaptation process is not. desirable because there is verylittle directional interference to be eliminated in the first place, andthe adaptation process searches in vain for new filter weights toeliminate the uncorrelated noise, which often results in cancelling thesource signal component of the received signal.

By contrast, the directional-interference-based inhibition mechanismwill inhibit the adaptation process in such a case because the strengthof directional interference as reflected in the reference channel poweraverage will be smaller than the normalized main channel power average,producing a positive normalized power difference. The adaptive processis inhibited as a result until there is some directional interference tobe eliminated.

FIG. 9 shows the frequency-selective constraint adaptive filter togetherwith the difference unit in accordance with a preferred embodiment ofthe present invention. The frequency-selective constraint adaptivefilter 101 includes a finite impulse response (FIR) filter 102, an LMSweight updating unit 103 and a frequency-selective weight-constraintunit 104. In an alternative embodiment, an infinite impulse response(IIR) filter can be used instead of the FIR filter.

A flat-frequency reference channel 105 passes through FIR filter 102whose filter weights are adjusted to produce a cancelling signal 106which closely approximates the actual interference signal componentpresent in a main channel 107. In a preferred embodiment, the mainchannel is obtained from the main channel matrix unit after a delay inorder to synchronize the main channel with the cancelling signal. Ingeneral, there is a delay between the main channel and the cancellingsignal because the cancelling signal is obtained by processing referencechannels through extra stages of delay, i.e., the decolorization filtersand adaptive filters. In an alternative embodiment, the main channeldirectly from the main channel matrix unit may be used if the delay isnot significant.

A difference unit 108 subtracts cancelling signal 106 from main channel107 to generates an output signal 109. Adaptive filter 101 adjustsfilter weights, W₁-W_(n), to minimize the power of the output signal.When the filter weights settle, output signal 109 generates the sourcesignal substantially free of the actual interference signal componentbecause cancelling signal 106 closely tracks the interference signalcomponent. Output signal 109 is sent to the output D/A unit to producean analog output signal. Output signal 109 is also used to adjust theadaptive filter weights to further reduce the interference signalcomponent.

There are many techniques to continuously update the values of thefilter weights. The preferred embodiment uses the Least Mean-Square(LMS) algorithm which minimize the mean-square value of the differencebetween the main channel and the cancelling signal, but in analternative embodiment, other algorithms such as Recursive Least Square(RLS) can also be used.

Under the LMS algorithm, the adaptive filter weights are updatedaccording to the following:

W_(p)(n+1)=W_(p)(n)+2μr(n-p)e(n)

where n is a discrete time index; W_(p) is a p-th filter weight of theadaptive filter; e(n) is a difference signal between the main channelsignal and the cancelling signal; r(n) is a reference channel; and μ isan adaptation constant that controls the speed of adaptation.

FIG. 10 depicts a preferred embodiment of the frequency-selectiveweight-constraint unit. The frequency-selective weight-control unit 110includes a Fast Fourier Transform (FFT) unit 112, a set of frequencybins 114, a set of truncating units 115, a set of storage cells 116, andan Inverse Fast Fourier Transform (IFFT) unit 117, connected in series.

The FFT unit 112 receives adaptive filter weights 111 and performs theFFT of the filter weights 111 to obtain frequency representation values113. The frequency representation values are then divided into a set offrequency bands and stored into the frequency bins 114 a-114 h. Eachfrequency bin stores the frequency representation values within aspecific bandwidth assigned to each bin. The values represent theoperation of the adaptive filter with respect to a specific frequencycomponent of the source signal. Each of the truncating units 115 a-115 hcompares the frequency representation values with a threshold assignedto each bin, and truncates the values if they exceeds the threshold. Thetruncated frequency representation values are temporarily stored in 116a-116 h before the IFFT unit 117 converts them back to new filter weightvalues 118.

In addition to the inhibiting mechanism based on directionalinterference, the frequency-selective weight-constraint unit furthercontrols the adaptation process based on the frequency spectrum of thereceived source signal. Once the adaptive filter starts working, theperformance change in the output of the filter, better or worse, becomesdrastic. Uncontrolled adaptation can quickly lead to a drasticperformance degradation.

The weight-constraint mechanism is based on the observation that a largeincrease in the adaptive filter weight values hints signal leakage. Ifthe adaptive filter works properly, there is no need for the filter toincrease the filter weights to large values. But, if the filter is notworking properly, the filter weights tend to grow to large values.

One way to curve the growth is to use a simple truncating mechanism totruncate the values of filter weights to predetermined threshold values.In this way, even if the overall signal power may be high enough totrigger the inhibition mechanism, the weight-constraint mechanism canstill prevent the signal leakage.

For narrow band signals, such as a speech signal or a tonal signal,having their power spectral density concentrated in a narrow frequencyrange, signal leakage may not be manifested in a large growth of thefilter weight values in the time domain. However, the filter weightvalues in the frequency domain will indicate some increase because theyrepresent the operation of the adaptive filter in response to a specificfrequency component of the source signal. The frequency-selectiveweight-constraint unit detects that condition by sensing a largeincrease in the frequency representation values of the filter weights.By truncating the frequency representation values in the narrowfrequency band of interest and inverse-transforming them back to thetime domain, the unit acts to prevent the signal leakage involvingnarrow band signals.

The system described herein may be implemented using commerciallyavailable digital signal processing (DSP) systems such as Analog Device2100 series.

FIG. 11 shows a flow chart depicting the operation of a program for aDSP processor in accordance with a preferred embodiment of the presentinvention.

After the program starts at step 100, the program initializes registersand pointers as well as buffers (step 110). The program then waits foran interrupt from a sampling unit requesting for processing of samplesreceived from the array of sensors (step 120). When the sampling unitsends an interrupt (step 131) that the samples are ready, the programreads the sample values (step 130) and stores the values (step 140). Theprogram filters the stored values using a routine implementing a tappeddelay line and stores the filtered input values (step 141).

The program then retrieves the filtered input values (step 151) and mainchannel matrix coefficients (step 152) to generate a main channel (step150) by multiplying the two and to store the result (step 160).

The program retrieves the filtered input values (step 171) and referencechannel matrix coefficients (step 152) to generate a reference channel(reference channel #1) by multiplying the two (step 170) and to storethe result (step 180). Steps 170 and 180 are repeated to generate allother reference channels (step 190).

The program retrieves one of the reference channels (step 201) anddecolorization filter coefficients for the corresponding referencechannel (step 202) to generate a flat-frequency reference channel bymultiplying the two (step 200) and stores the result (step 210). Steps200 and 210 are repeated for all other reference channels (step 220).

The program retrieves one of the flat-frequency reference channels (step231) and adaptive filter coefficients (step 232) to generate cancellingsignal (step 230) by multiplying the two and to store the result (step240). Steps 230 and 240 are repeated for all other reference channels togenerate more cancelling signals (step 250).

The program retrieves cancelling signals (steps 262-263) to subtractthem from the main channel (retrieved at step 261) to cancel theinterference signal component in the main channel (step 260). The outputis send to a D/A unit to reproduce the signal without interference inanalog form (step 264). The output is also stored (step 270).

The program calculates the power of a reference channel sample (step281) and retrieves an old reference channel power average (step 282).The program multiplies the sample power by α and the old power averageby (1−α), and sums them (step 280), and stores the result as a new poweraverage (step 290). This process is repeated for all other referencechannels (step 300) and the total sum of power averages of all referencechannels is stored (step 310).

The program multiplies the power of a main channel sample (retrieved atstep 321) by a and an old main channel power average (retrieved at step322) by (1−α), sums them (step 320) and stores them as a new mainchannel power average (step 330).

The program then multiplies the main channel power with a threshold toobtain a normalized main channel power average (step 340). The programsubtracts the total reference channel power average (retrieved at step341) from the normalized main channel power average to produce adifference (step 350). If the difference is positive, the program goesback to step 120 where it simply waits for another samples.

If the difference is negative, the program enters a weight-updatingroutine. The program calculates a new filter eight by adding[2×adaptation constant×reference channel sample (retrieved at step361)×output (retrieved at step 362)] to an old filter weight (retrievedat step 363) to update the weight (step 360) and stores the result (step370).

The program performs the FFT of the new filter weights to obtain theirfrequency representation (step 380). The frequency representation valuesare divided into several frequency bands and stored into a set offrequency bins (step 390). The frequency representation values in eachbin are compared with a threshold associated with each frequency bin(step 400). If the values exceed the threshold, the values are truncatedto the threshold (step 410). The program performs the IFFT to convertthe truncated frequency representation values back to filter weightvalues (step 420) and stores them (step 430). The program repeats theweight-updating routine, steps 360-430, for all other reference channelsand associated adaptive filters (step 440). The program then goes backto step 120 to wait for an interrupt for a new round of processingsamples (step 450).

While the invention has been described with reference to preferredembodiments, it is not limited to those embodiments. It will beappreciated by those of ordinary skill in the art that modifications canbe made to the structure and form of the invention without departingfrom its spirit and scope which is defined in the following claims.

What is claimed is:
 1. A method for processing digital input datarepresenting signals containing a source signal from a signal sourceon-axis from an array of sensors as well as interference signals frominterference sources located off-axis from the signal source and forproducing digital output data representing the source signal withreduced interference signals relative to the source signal, comprisingthe steps of: generating a main channel from the digital input data, themain channel representing signals received in the direction of thesignal source and having a source signal component and an interferencesignal component; generating at least one reference channel from thedigital input data, each reference channel representing signals receivedin directions other than that of the signal source; adaptively filteringsaid at least one reference channel using filter weight values togenerate a cancelling signal approximating the interference signalcomponent in the main channel; generating the digital output data bysubtracting the cancelling signal from the main channel; deriving newfilter weight values so that the difference between the main channel andthe cancelling signal is minimized; and truncating the new filter weightvalues to predetermined threshold values when each of the new filterweight values exceeds the corresponding threshold value.
 2. The methodof claim 1, further comprising the step of filtering said at least onereference channel so that it has a substantially flat frequencyspectrum.
 3. The method of claim 1, further comprising the step ofinhibiting the generation of the cancelling signal when a normalizedpower difference between the main channel and said at least onereference channel is positive.
 4. A method for processing digital inputdata representing signals containing a source signal from a signalsource on-axis from an array of sensors as well as interference signalsfrom interference sources located off-axis from the signal source andfor producing digital output data representing the source signal withreduced interference signals relative to the source signal, comprisingthe steps of: generating a main channel from the digital input data, themain channel representing signals received in the direction of thesignal source and having a source signal component and an interferencesignal component; generating at least one reference channel from thedigital input data, each reference channel representing signals receivedin directions other than that of the signal source; filtering said atleast one reference channel using filter weight values to generate acancelling signal approximating the interference signal component in themain channel; generating the digital output data by subtracting thecancelling signal from the main channel; deriving new filter weightvalues so that the difference between the main channel and thecancelling signal is minimized; and constraining the new filter weightvalues by converting the new filter weight values to frequencyrepresentation values, truncating the frequency representation values topredetermined threshold values, and converting them back to filterweight values.
 5. The method of claim 4 wherein constraining the newfilter weight values comprises: generating frequency representationvalues of the new filter weight values; divide the frequencyrepresentation values into a plurality of frequency bins; truncating thefrequency representation values in each frequency bin if they exceed apredetermined threshold value associated with each frequency bin; andconverting the frequency representation values back to filter weightvalues.
 6. The method of claim 5 wherein generating the frequencyrepresentation is done by using the Fast Fourier Transform, andconverting back is done by using the Inverse Fast Fourier Transform. 7.The method of claim 4, further comprising the step of filtering said atleast one reference channel so that it has a substantially flatfrequency spectrum.
 8. The method of claim 4, further comprising thestep of inhibiting the generation of the cancelling signal when anormalized power difference between the main channel and said at leastone reference channel is positive.
 9. A method for receiving a sourcesignal from a signal source as well as interference signals frominterference sources and for producing an output signal with reducedinterference signals relative to the source signal, comprising the stepsof: receiving analog signals from a sensor array of spatiallydistributed sensors; sampling the analog signals to convert them todigital form; generating a main channel representing signals received inthe direction of the signal source, the main channel having a sourcesignal component and an interference signal component; generating atleast one reference channel, each reference channel representing signalsreceived in directions other than that of the signal source;, filteringsaid at least one reference channel using filter weight values togenerate a cancelling signal approximating the interference signalcomponent in the main channel; generating a digital output signal bysubtracting the cancelling signal from the main channel; converting thedigital output signal to analog form; deriving new filter weight valuesso that the difference between the main channel and the cancellingsignal is minimized; and truncating the new filter weight values topredetermined threshold values when each of the new filter weight valuesexceeds the corresponding threshold value.
 10. The method of claim 9,further comprising the step of filtering said at least one referencechannel so that it has a substantially flat frequency spectrum.
 11. Themethod of claim 9, further comprising the step of inhibiting thegeneration of the cancelling signal when a normalized power differencebetween the main channel and said at least one reference channel ispositive.
 12. A method for receiving a source signal from a signalsource as well as interference signals from interference sources and forproducing an output signal with reduced interference signals relative tothe source signal, comprising the steps of: receiving analog signalsfrom a sensor array of spatially distributed sensors; sampling theanalog signals to convert them to digital form; generating a mainchannel representing signals received in the direction of the signalsource, the main channel having a source signal component and aninterference signal component; generating at least one referencechannel, each reference channel representing signals received indirections other than that of the signal source; filtering said at leastone reference channel using filter weight values to generate acancelling signal approximating the interference signal component in themain channel; generating a digital output signal by subtracting thecancelling signal from the main channel; converting the digital outputsignal to analog form; deriving new filter weight values so that thedifference between the main channel and the cancelling signal isminimized; and constraining the new filter weight values by convertingthe new filter weight values to frequency representation values,truncating the frequency representation values to predeterminedthreshold values, and converting them back to filter weight values. 13.The method of claim 12 wherein constraining the new filter weight valuescomprises: generating frequency representation values of the new filterweight values; divide the frequency representation values into aplurality of frequency bins; truncating the frequency representationvalues in each frequency bin if they exceed a predetermined thresholdvalue associated with each frequency bin; and converting the frequencyrepresentation values back to filter weight values.
 14. The method ofclaim 13 wherein generating frequency representation values is done byusing the Fast Fourier Transform, and converting them back to filterweight values is done by using the Inverse Fast Fourier Transform. 15.The method of claim 12, further comprising the step of filtering saidleast one reference channel so that it has a substantially flatfrequency spectrum.
 16. The method of claim 12, further comprising thestep of inhibiting the generation of the cancelling signal when anormalized power difference between the main channel and said at leastone reference channel is positive.